Traffic matrix completion by weighted tensor nuclear norm minimization and time slicing

Abstract

A traffic matrix (TM) plays an essential role in many network analysis tasks. Since obtaining the full TM by direct observation is challenging, many studies have focused on recovering the TM from the partial observation. These existing methods, which achieve high recovery accuracy using the spatio-temporal characteristics of TM, are computationally expensive and/or require prior training. We propose a new TM completion method based on a nonlinear, nonconvex optimization for a weighted tensor nuclear norm minimization with tensor construction based on the intrinsic periodicity of TM. Our tensor construction method does not require complicated spatio-temporal characteristic estimation and prior training. The experimental results on two real-world traffic data and various data-missing scenarios show that the proposed method can achieve the comparable recovery capability as the conventional methods with a significantly simpler problem formulation.